Combining deep neural networks (DNNs) based perception and control has long been a crucial research area for a number of robotic application domains, such as drones [1]}, [2]}, self-driving cars [3]}, [4]}, [5]}, robotic arms [6]}, [7]}, [8]}, etc. To achieve this objective, a branch of researchers [3]}, [6]}, [11]} adopted end-to-end learning frameworks, allowing robots to perform actions based on pixels of high-dimensional, unstructured RGB images. These end-to-end learning frameworks bypass
explicit visual feature learning processes and directly maps raw visual inputs to robotic actions. However, such end-to-end design lacks the flexibility of adjusting or fine-tuning, since the perception and control parts are developed as a whole. In recent years, another branch of researchers shifted their attention to modular designs, which decompose end-to-end learning frameworks to perception and control modules, allowing them to be developed separately. To bridge these modules, appropriate intermediate feature representations, which are usually referred to as mid-level representations [12]}, [13]}, [14]}, [15]}, are used to deliver various types of information from the perception module to the control module, and form the basis of modular frameworks for many learning-based systems.
